Fred And The Vaccines¶

Author: Justin Garza

Date: See below

Description:
This notebook explores the vaccines impact on St Louis

Content Warning:
If you find discussions of death (or injury) or its underlying factors distressing, please proceed with caution or consider whether this content is right for you.

In [1]:
from datetime import datetime
from IPython.display import display
from IPython.display import Markdown as MD
current_date = datetime.now().strftime('%Y-%m-%d')
version = datetime.now().strftime('%Y%m%d.%H%M')
display(MD(f"**Date:** {current_date}"))
display(MD(f"**version:** {version}"))

Date: 2025-02-13

version: 20250213.2243

Haste Makes Waste¶

history is rife with examples of when Haste Makes Waste

  • 1. Ford Pinto (1971-1980)

    • Flaw: Gas tank placement made it prone to explosions in rear-end collisions.
    • Cause: Rushed production to compete with Japanese imports; Ford ignored safety concerns.
    • Consequence: Fatalities, lawsuits, and one of the most infamous recalls in history.

  • 2. Windows Vista (2007)

    • Flaw: Poor performance, hardware incompatibility, and excessive security pop-ups.
    • Cause: Microsoft rushed it out after multiple delays, leading to an unfinished OS.
    • Consequence: Heavy criticism, forcing Microsoft to fast-track Windows 7.

  • 3. Samsung Galaxy Note 7 (2016)

    • Flaw: Overheating batteries caused explosions.
    • Cause: Samsung rushed to beat the iPhone 7, leading to a faulty design.
    • Consequence: Global recall, airline bans, and a $5 billion loss.

  • 4. Boeing 737 MAX (2017)

    • Flaw: Faulty MCAS software led to two fatal crashes.
    • Cause: Boeing cut corners to compete with Airbus and downplayed safety risks.
    • Consequence: 346 deaths, a worldwide grounding, and massive reputational damage.

  • 5. Apple Maps (2012)

    • Flaw: Inaccurate navigation, missing landmarks, and distorted maps.
    • Cause: Apple rushed to replace Google Maps in iOS 6 with an incomplete product.
    • Consequence: Public backlash, executive firings, and a rare apology from Tim Cook.

  • 6. Sony PlayStation 2 (2000)

    • Flaw: Disc read errors and overheating issues.
    • Cause: Sony rushed launch to beat Microsoft’s Xbox.
    • Consequence: Lawsuits, free repairs, but eventually became the best-selling console.

  • 7. Cyberpunk 2077 (2020)

    • Flaw: Game-breaking bugs and poor performance, especially on last-gen consoles.
    • Cause: CD Projekt Red rushed it after multiple delays.
    • Consequence: Mass refunds, delisting from PlayStation Store, and loss of trust.

  • 8. Xbox 360 & Red Ring of Death (2005)

    • Flaw: Hardware overheating caused widespread system failures.
    • Cause: Microsoft rushed production to beat Sony’s PS3, leading to poor cooling design.
    • Consequence: Over $1 billion in repair costs and damaged brand reputation.

Wasn't the Covid-19 Vaccine also rushed?¶

it was called project WarpSpeed...right?

Setup¶

In this section, we prepare the notebook by importing necessary libraries, configuring settings, and setting up directories for data and outputs. The setup ensures the environment is ready for data analysis and visualization.

In [2]:
# this code to will import all the things i need for this notebook

import os
import re
import math

import numpy as np
import pandas as pd

# for the notebook rendering 
from IPython.display import display, HTML
from IPython.display import Markdown as MD

# Graphs and Charts
import seaborn as sns
import plotly.express as px

# pandas Settings/Options
pd.set_option("display.max_rows", None) 
pd.set_option("display.max_columns", None)
pd.set_option('display.width', 9000)
pd.set_option('max_colwidth', 400)
pd.set_option('display.float_format', '{:.3f}'.format)

# colormap 
heatmapCM = sns.color_palette('Spectral_r', as_cmap=True)


## directories 
DIR = os.getcwd()
print(f'{DIR=}')

DataDIR = os.path.join(DIR,'data')
OutDIR = os.path.join(DIR,'docs')

if not os.path.exists(DataDIR):
    print('***DATA FOLDER IS MISSING***')

if not os.path.exists(OutDIR):
    os.makedirs(OutDIR)

DIR='C:\\Users\\JGarza\\GitHub\\Fred_And_The_Vaccines'

Fred of St Louis - Civilian Labor Force - With a Disability Data¶

Federal Reserve Economic Data

Getting the Data¶

Source Federal Reserve Bank of St Louis

  1. Go to Civilian Labor Force - With a Disability, 16 Years and over
  2. click download
  3. download as CSV
In [3]:
# import Vaccine Data 
df = pd.read_csv(os.path.join(DataDIR,'LNU01074597.csv'))

df = df.rename(columns={'observation_date':'date'})
df = df.rename(columns={'LNU01074597':'count'})

# display(df.head(5))
In [4]:
title = 'Civilian Labor Force - With a Disability, 16 Years and over'

fig = px.line(
    df,
    x='date',
    y='count',
    height=750 ,
    title=title
    )
fig.update_layout(template="plotly_dark")

temp = df[df['date'] < '2020-01-01'].copy()
min_temp = temp['count'].min()
max_temp = temp['count'].max()

# adding Min Line
fig.add_shape(
    type="line",
    x0='2009-01-01', 
    x1='2024-01-01', 
    y0=min_temp,
    y1=min_temp, 
    line=dict(color="Grey", width=2, dash="dash"),  # Line style
    xref="x", yref="y"          # Reference axes
)

# adding Max Line
fig.add_shape(
    type="line",
    x0='2009-01-01', 
    x1='2024-01-01', 
    y0=max_temp,
    y1=max_temp, 
    line=dict(color="Grey", width=2, dash="dash"),  # Line style
    xref="x", yref="y"          # Reference axes
)

fig.add_shape(
    type="circle",
    x0='2020-11-01', x1='2021-03-01',  # Keep x values the same for a point
    y0=5846-100, y1=5846+100,  # Define the vertical range
    line=dict(color="Red", width=2),  # Circle border style
    xref="x", yref="y"
)

fig.add_shape(
    type="line",
    x0='2020-3-07', x1='2020-03-07',  # Keep x values the same for a point
    y0=max_temp, y1=min_temp,  # Define the vertical range
    line=dict(color="Green", width=2),  # Circle border style
    xref="x", yref="y"
)

display(MD(f'### {title}'))
display(HTML('<h4><span style="color: grey;"><b>Grey Lines</b></span> are the max and min values from 2008-06-01 and 2020-01-01</h4>'))
display(HTML('<h4><span style="color: green;"><b>Green Line</b></span> represents the first time COVID-19 was detected in St. Louis</h4>'))
display(HTML('<p style="text-indent: 40px;"><a href="https://www.stlpr.org/health-science-environment/2020-03-08/first-missouri-coronavirus-case-is-in-st-louis-county">First Missouri Coronavirus Case Is In St. Louis County</p>'))
display(HTML('<h4><span style="color: red;"><b>Red Circle</b></span> marks an event that caused disabilities to increase</h4>'))
fig.show()

Civilian Labor Force - With a Disability, 16 Years and over¶

Grey Lines are the max and min values from 2008-06-01 and 2020-01-01

Green Line represents the first time COVID-19 was detected in St. Louis

First Missouri Coronavirus Case Is In St. Louis County

Red Circle marks an event that caused disabilities to increase

Hypothesis¶

  • There should be a causing event that occured in St Louis on January 2021 ?

News (ksdk)¶

  • City of St. Louis to get its first shipment of COVID-19 vaccines Tuesday
    • Published: 4:11 PM CST January 25, 2021
    • Updated: 5:26 PM CST January 26, 2021

ChatGPT¶

  • .
  • .

Google Trends¶

In Google Trends there was a small increase in the search terms, but it really ramped up after.

myocarditis: a known *rare side effect of the mRNA vaccines.
Vaccine Pain: a common side effect of the mRNA vaccine.
VAERS: Vaccine Adverse Event Reporting System

💉: when the vaccine was avaliable

🦠: when the covid-19 virus was first detected in St. Louis Mo

  • .
  • .

Possible - Conclusion¶

  • Something was wrong with the batch of vaccines the St Luois got
  • Something was wrong with all the Vaccines
  • People are just claiming disablity to get out of work
    • but if they wanted to get out of work, why not just use the virus as an excuse ?
  • This was a delayed reaction from the covid virus, or a new variant of the covid virus
  • The Data is bad
  • This is a correlation (not causation), and there is no proof linking the vaccine with disabilities.
    • Correlation:
      • Example: Ice cream sales and drowning incidents increase at the same time.
        • This is a correlation because there is no direct causal link between ice cream sales and drowning. Both are influenced by a third factor (hot weather).
    • Causation:
      • Example: Ice cream sales cause drowning incidents.
        • This is not true causation because buying ice cream does not directly lead to drowning. The relationship exists due to an external factor.
      • Example: An impact to your body causes a bruise in the same location where you were hit.
        • This is causation because the impact (X) directly leads to the bruise (Y), with a clear connection in timing, location, and biological response.
    • The Vaccine's relationship to the Disabilities is a causation (in the data/chart above).
      • They have a clear connection in timing, location, and biological response.
      • There was no other large event going on that could cause this jump in disabilities.
      • This relationship can also been seen in 4 other locations (Denmark, Finland, Norway, and UK) ... see below.
    • this is the Denying Causation or Causal Skepticism Fallacy

if you want to keep digging¶

  • Excess deaths, the silence - Dr John Campbell and Dr Vibeke Manniche (Youtube)
    • Denmark, Finland, Norway, UK
    • .
    • .

more videos with Dr John Campbell and Dr Vibeke Manniche¶

  • Batch dependent safety - Dr John Campbell (Youtube)
  • Viral vaccine paper, Dr Vibeke Manniche - Dr John Campbell (Rumble)
  • Batch-dependent safety of the BNT162b2 mRNA COVID-19 vaccine - By Dr Vibeke Manniche (Youtube)